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Dimitris Fotakis, Panagiotis Patsilinakos, Eleni Psaroudaki and Michalis Xefteris
In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian P...
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Mizuki Asano, Takumi Miyoshi and Taku Yamazaki
Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices ...
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D. Criado-Ramón, L. G. B. Ruiz and M. C. Pegalajar
Pattern sequence-based models are a type of forecasting algorithm that utilizes clustering and other techniques to produce easily interpretable predictions faster than traditional machine learning models. This research focuses on their application in ene...
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Carla Sahori Seefoo Jarquin, Alessandro Gandelli, Francesco Grimaccia and Marco Mussetta
Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent ...
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Massimo Pacella, Matteo Mangini and Gabriele Papadia
Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-inte...
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Renjie Chen and Nalini Ravishanker
With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing t...
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Peng-Yeng Yin
Air pollution has been a global issue that solicits proposals for sustainable development of social economics. Though the sources emitting pollutants are thoroughly investigated, the transportation, dispersion, scattering, and diminishing of pollutants i...
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Jasper Mohr, Andreas Tewes, Hella Ahrends and Thomas Gaiser
(1) Background: The relation between the sub-field heterogeneity of soil properties and high-resolution satellite time series data might help to explain spatiotemporal patterns of crop growth, but detailed field studies are seldom. (2) Methods: Normalize...
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Daiwei Zhang, Chunyang Ying, Lei Wu, Zhongqiu Meng, Xiaofei Wang and Youhua Ma
Timely and accurate extraction of crop planting structure information is of great importance for food security and sustainable agricultural development. However, long time series data with high spatial resolution have a much larger data volume, which ser...
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Chen Wang, Si-jia Zhao, Zong-qiang Ren and Qi Long
Classifying a time series is a fundamental task in temporal analysis. This provides valuable insights into the temporal characteristics of data. Although it has been applied to traffic flow and individual-centered accessibility analysis, it has yet to be...
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